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Improved analysis of probability estimates in model-based diagnosis

 

The core objective of model-based diagnosis is to identify candidate diagnoses which explain the observed symptoms. Usually there are multiple such candidate diagnoses and a model-based diagnostic engine proposes additional measurements to better isolate the true diagnosis. An objective of such an algorithm is to identify this diagnosis in minimum average expected cost. Minimizing this cost requires having accurate probability estimates for the candidate diagnoses. Most diagnostic engines utilize sequential diagnosis combined with Bayes Rule to determine the posterior probability of a candidate diagnosis given a measurement outcome. Unfortunately, one of the terms of Bayes rule, the conditional probability of a measurement outcome given a candidate diagnosis, must often be estimated (noted as $epsilon$ in most formulations). This paper presents a reformulation of the sequential diagnosis process used in diagnostic engines and shows how different epsilon policies lower diagnostic cost.

 
citation

de Kleer, J. Improved analysis of probability estimates in model-based diagnosis. Twenty-Second AAAI Conference on Artificial Intelligence (AAAI-2007); 2007 July 22-26; Vancouver; BC; Canada.